Journal of Shanghai Jiao Tong University (Medical Science) ›› 2026, Vol. 46 ›› Issue (10): 1383-1391.doi: 10.3969/j.issn.1674-8115.2026.10.008

• Clinical research • Previous Articles    

Risk factors for venous thromboembolism in patients with metastatic bone tumors: development and validation of a machine learning-based prediction model

Ruan Shunyi1, Sun Yi2, Wang Qiong1, Chang Yujie1, Gu Yifeng3, Xu Yongming4, Wang Zhiyu1,5(), Zhao Hui1,5()   

  1. 1.Department of Internal Oncology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
    2.Department of Radiotherapy for Oncology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
    3.Department of Radiology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
    4.Department of Pain Management, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
    5.Clinical Diagnosis and Treatment Center for Metastatic Bone Tumors, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
  • Received:2026-04-01 Accepted:2026-05-18 Online:2026-10-28 Published:2026-10-28
  • Contact: Wang Zhiyu, Zhao Hui E-mail:areyouwangzhiyu@163.com;zhao-hui@sjtu.edu.cn
  • Supported by:
    “Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20172024)

Abstract:

Objective ·To identify independent risk factors for venous thromboembolism (VTE) in patients with metastatic bone tumors and to develop and validate a machine learning-based prediction model. Methods ·This retrospective cohort study included 439 patients with metastatic bone tumors treated at the Clinical Diagnosis and Treatment Center for Metastatic Bone Tumors, Shanghai Sixth People's Hospital, from January 2012 to December 2022 for model training and internal validation. Collected data included demographic characteristics (sex and age at diagnosis of metastatic bone tumors), primary tumor sites, Karnofsky performance status, treatment modalities (radiotherapy and surgery), spinal cord compression, admission complete blood count parameters (red blood cell count, white blood cell count, hemoglobin, and platelet count), coagulation indices (D-dimer, fibrinogen, and prothrombin time), and sites of bone metastases. Patients were divided into the VTE group (n=153) and the non-VTE group (n=286) based on VTE outcomes. Multivariate Logistic regression was used to identify risk factors. Five machine learning models—CatBoost, LightGBM, XGBoost, Gaussian process, and Logistic regression—were trained and evaluated using ten-fold cross-validation to predict VTE risk and identify the optimal model for clinical risk stratification. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A prospective cohort including 65 patients with metastatic bone tumors admitted between January 2023 and December 2024 was used to validate the performance of the model with the highest AUC. Shapley additive explanations (SHAP) was employed for clinical interpretation of model features. Results ·There were no significant differences in age, sex distribution, or Karnofsky performance status between the VTE group and the non-VTE group (all P>0.05). The most common primary tumors were lung cancer (40.3%), breast cancer (12.3%), and prostate cancer (7.3%). The most common sites of bone metastases were the spine (64.7%), pelvis (53.5%), ribs and clavicle (33.9%), and lower limbs (28.7%). Three independent risk factors were identified: lower limb bone metastases (OR=1.89, 95%CI 1.17‒3.07, P=0.010), elevated D-dimer levels (OR=1.06, 95%CI 1.02‒1.09, P<0.001), and prolonged prothrombin time (OR=1.09, 95%CI 1.01‒1.17, P=0.034). CatBoost demonstrated the best performance, with an AUC of 0.82 (95%CI 0.73‒0.90) in the internal test set and 0.80 (95%CI 0.67‒0.93) in the prospective validation set. SHAP analysis confirmed D-dimer as the most important predictor (mean |SHAP|=1.41). Conclusion ·Lower limb bone metastases, elevated D-dimer levels, and prolonged prothrombin time are independent risk factors for VTE in patients with metastatic bone tumors. The CatBoost-based prediction model demonstrated robust performance in prospective validation, showing potential for clinical application in predicting VTE risk in patients with metastatic bone tumors.

Key words: metastatic bone tumor, venous thromboembolism (VTE), risk factor, machine learning, CatBoost, prediction model

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